Overview

Dataset statistics

Number of variables38
Number of observations7834
Missing cells21167
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory304.0 B

Variable types

Categorical27
Numeric1
Text9
DateTime1

Alerts

Any Opioid is highly overall correlated with Date Type and 1 other fieldsHigh correlation
Date Type is highly overall correlated with Any OpioidHigh correlation
Ethnicity is highly overall correlated with Manner of Death and 1 other fieldsHigh correlation
Fentanyl is highly overall correlated with Any OpioidHigh correlation
Heroin is highly overall correlated with Heroin/Morph/CodeineHigh correlation
Heroin/Morph/Codeine is highly overall correlated with HeroinHigh correlation
Manner of Death is highly overall correlated with EthnicityHigh correlation
Morphine (Not Heroin) is highly overall correlated with EthnicityHigh correlation
Race is highly imbalanced (78.4%)Imbalance
Ethnicity is highly imbalanced (55.7%)Imbalance
Manner of Death is highly imbalanced (99.6%)Imbalance
Heroin death certificate (DC) is highly imbalanced (75.2%)Imbalance
Fentanyl Analogue is highly imbalanced (57.6%)Imbalance
Oxycodone is highly imbalanced (55.0%)Imbalance
Oxymorphone is highly imbalanced (87.6%)Imbalance
Hydrocodone is highly imbalanced (88.2%)Imbalance
Methadone is highly imbalanced (56.8%)Imbalance
Meth/Amphetamine is highly imbalanced (89.1%)Imbalance
Amphet is highly imbalanced (79.6%)Imbalance
Tramad is highly imbalanced (81.1%)Imbalance
Hydromorphone is highly imbalanced (94.3%)Imbalance
Morphine (Not Heroin) is highly imbalanced (96.0%)Imbalance
Xylazine is highly imbalanced (70.3%)Imbalance
Gabapentin is highly imbalanced (85.1%)Imbalance
Opiate NOS is highly imbalanced (91.5%)Imbalance
Other Opioid is highly imbalanced (91.4%)Imbalance
Ethnicity has 6763 (86.3%) missing valuesMissing
Other Significant Conditions has 7089 (90.5%) missing valuesMissing
Other has 7315 (93.4%) missing valuesMissing

Reproduction

Analysis started2024-04-18 17:56:09.625128
Analysis finished2024-04-18 17:56:17.215657
Duration7.59 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Date Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
Date of death
6862 
Date reported
972 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters101842
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDate of death
2nd rowDate of death
3rd rowDate of death
4th rowDate reported
5th rowDate reported

Common Values

ValueCountFrequency (%)
Date of death 6862
87.6%
Date reported 972
 
12.4%

Length

2024-04-18T17:56:17.364868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:17.605578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
date 7834
34.8%
of 6862
30.5%
death 6862
30.5%
reported 972
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 16640
16.3%
t 15668
15.4%
a 14696
14.4%
14696
14.4%
D 7834
7.7%
o 7834
7.7%
d 7834
7.7%
f 6862
6.7%
h 6862
6.7%
r 1944
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16640
16.3%
t 15668
15.4%
a 14696
14.4%
14696
14.4%
D 7834
7.7%
o 7834
7.7%
d 7834
7.7%
f 6862
6.7%
h 6862
6.7%
r 1944
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16640
16.3%
t 15668
15.4%
a 14696
14.4%
14696
14.4%
D 7834
7.7%
o 7834
7.7%
d 7834
7.7%
f 6862
6.7%
h 6862
6.7%
r 1944
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16640
16.3%
t 15668
15.4%
a 14696
14.4%
14696
14.4%
D 7834
7.7%
o 7834
7.7%
d 7834
7.7%
f 6862
6.7%
h 6862
6.7%
r 1944
 
1.9%

Age
Real number (ℝ)

Distinct68
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.07327
Minimum14
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:17.833092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile24
Q133
median43
Q353
95-th percentile63
Maximum87
Range73
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.529796
Coefficient of variation (CV)0.29089492
Kurtosis-0.91059405
Mean43.07327
Median Absolute Deviation (MAD)10
Skewness0.078158477
Sum337436
Variance156.99578
MonotonicityNot monotonic
2024-04-18T17:56:18.111123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 231
 
2.9%
33 226
 
2.9%
54 216
 
2.8%
35 214
 
2.7%
50 204
 
2.6%
37 203
 
2.6%
51 202
 
2.6%
40 201
 
2.6%
34 196
 
2.5%
48 195
 
2.5%
Other values (58) 5746
73.3%
ValueCountFrequency (%)
14 2
 
< 0.1%
15 2
 
< 0.1%
16 3
 
< 0.1%
17 11
 
0.1%
18 15
 
0.2%
19 29
 
0.4%
20 48
0.6%
21 74
0.9%
22 96
1.2%
23 100
1.3%
ValueCountFrequency (%)
87 1
 
< 0.1%
84 1
 
< 0.1%
81 2
 
< 0.1%
80 1
 
< 0.1%
78 1
 
< 0.1%
77 1
 
< 0.1%
75 2
 
< 0.1%
74 2
 
< 0.1%
73 9
0.1%
72 8
0.1%

Sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
Male
5828 
Female
2006 

Length

Max length6
Median length4
Mean length4.5121266
Min length4

Characters and Unicode

Total characters35348
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 5828
74.4%
Female 2006
 
25.6%

Length

2024-04-18T17:56:18.632465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:18.877880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 5828
74.4%
female 2006
 
25.6%

Most occurring characters

ValueCountFrequency (%)
e 9840
27.8%
a 7834
22.2%
l 7834
22.2%
M 5828
16.5%
F 2006
 
5.7%
m 2006
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9840
27.8%
a 7834
22.2%
l 7834
22.2%
M 5828
16.5%
F 2006
 
5.7%
m 2006
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9840
27.8%
a 7834
22.2%
l 7834
22.2%
M 5828
16.5%
F 2006
 
5.7%
m 2006
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9840
27.8%
a 7834
22.2%
l 7834
22.2%
M 5828
16.5%
F 2006
 
5.7%
m 2006
 
5.7%

Race
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
White
6779 
Black
 
677
Black or African American
 
263
Unknown
 
40
Other
 
24
Other values (7)
 
51

Length

Max length32
Median length5
Mean length5.7291294
Min length5

Characters and Unicode

Total characters44882
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowWhite
2nd rowWhite
3rd rowWhite
4th rowWhite
5th rowWhite

Common Values

ValueCountFrequency (%)
White 6779
86.5%
Black 677
 
8.6%
Black or African American 263
 
3.4%
Unknown 40
 
0.5%
Other 24
 
0.3%
Asian Indian 21
 
0.3%
Asian, Other 20
 
0.3%
Other Asian 6
 
0.1%
American Indian or Alaska Native 1
 
< 0.1%
Hawaiian 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-18T17:56:19.083013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white 6779
78.1%
black 940
 
10.8%
american 265
 
3.1%
or 264
 
3.0%
african 263
 
3.0%
other 51
 
0.6%
asian 47
 
0.5%
unknown 40
 
0.5%
indian 22
 
0.3%
native 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 7381
16.4%
e 7099
15.8%
t 6832
15.2%
h 6831
15.2%
W 6779
15.1%
a 1544
 
3.4%
c 1468
 
3.3%
k 981
 
2.2%
l 941
 
2.1%
B 940
 
2.1%
Other values (18) 4086
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7381
16.4%
e 7099
15.8%
t 6832
15.2%
h 6831
15.2%
W 6779
15.1%
a 1544
 
3.4%
c 1468
 
3.3%
k 981
 
2.2%
l 941
 
2.1%
B 940
 
2.1%
Other values (18) 4086
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7381
16.4%
e 7099
15.8%
t 6832
15.2%
h 6831
15.2%
W 6779
15.1%
a 1544
 
3.4%
c 1468
 
3.3%
k 981
 
2.2%
l 941
 
2.1%
B 940
 
2.1%
Other values (18) 4086
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7381
16.4%
e 7099
15.8%
t 6832
15.2%
h 6831
15.2%
W 6779
15.1%
a 1544
 
3.4%
c 1468
 
3.3%
k 981
 
2.2%
l 941
 
2.1%
B 940
 
2.1%
Other values (18) 4086
9.1%

Ethnicity
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)0.5%
Missing6763
Missing (%)86.3%
Memory size61.3 KiB
Hispanic
789 
Other Spanish/Hispanic/Latino
231 
Not Spanish/Hispanic/Latino
 
48
Puerto Rican
 
2
Unknown
 
1

Length

Max length29
Median length8
Mean length13.387488
Min length7

Characters and Unicode

Total characters14338
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowHispanic
2nd rowHispanic
3rd rowOther Spanish/Hispanic/Latino
4th rowHispanic
5th rowHispanic

Common Values

ValueCountFrequency (%)
Hispanic 789
 
10.1%
Other Spanish/Hispanic/Latino 231
 
2.9%
Not Spanish/Hispanic/Latino 48
 
0.6%
Puerto Rican 2
 
< 0.1%
Unknown 1
 
< 0.1%
(Missing) 6763
86.3%

Length

2024-04-18T17:56:19.304986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:19.548190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
hispanic 789
58.4%
spanish/hispanic/latino 279
 
20.6%
other 231
 
17.1%
not 48
 
3.6%
puerto 2
 
0.1%
rican 2
 
0.1%
unknown 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 2696
18.8%
n 1631
11.4%
a 1628
11.4%
s 1347
9.4%
p 1347
9.4%
c 1070
 
7.5%
H 1068
 
7.4%
t 560
 
3.9%
/ 558
 
3.9%
h 510
 
3.6%
Other values (14) 1923
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2696
18.8%
n 1631
11.4%
a 1628
11.4%
s 1347
9.4%
p 1347
9.4%
c 1070
 
7.5%
H 1068
 
7.4%
t 560
 
3.9%
/ 558
 
3.9%
h 510
 
3.6%
Other values (14) 1923
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2696
18.8%
n 1631
11.4%
a 1628
11.4%
s 1347
9.4%
p 1347
9.4%
c 1070
 
7.5%
H 1068
 
7.4%
t 560
 
3.9%
/ 558
 
3.9%
h 510
 
3.6%
Other values (14) 1923
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2696
18.8%
n 1631
11.4%
a 1628
11.4%
s 1347
9.4%
p 1347
9.4%
c 1070
 
7.5%
H 1068
 
7.4%
t 560
 
3.9%
/ 558
 
3.9%
h 510
 
3.6%
Other values (14) 1923
13.4%
Distinct464
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:19.996082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length17
Mean length8.8821802
Min length3

Characters and Unicode

Total characters69583
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)2.8%

Sample

1st rowNORWICH
2nd rowHEBRON
3rd rowSHELTON
4th rowBLANDFORD
5th rowMILFORD
ValueCountFrequency (%)
new 958
 
9.9%
hartford 745
 
7.7%
haven 719
 
7.4%
waterbury 486
 
5.0%
bridgeport 410
 
4.2%
east 310
 
3.2%
britain 279
 
2.9%
west 230
 
2.4%
bristol 207
 
2.1%
meriden 187
 
1.9%
Other values (452) 5171
53.3%
2024-04-18T17:56:20.765370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 7346
 
10.6%
N 6441
 
9.3%
E 6283
 
9.0%
T 5488
 
7.9%
O 5444
 
7.8%
A 4994
 
7.2%
I 4070
 
5.8%
D 3691
 
5.3%
L 3001
 
4.3%
W 2994
 
4.3%
Other values (18) 19831
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 7346
 
10.6%
N 6441
 
9.3%
E 6283
 
9.0%
T 5488
 
7.9%
O 5444
 
7.8%
A 4994
 
7.2%
I 4070
 
5.8%
D 3691
 
5.3%
L 3001
 
4.3%
W 2994
 
4.3%
Other values (18) 19831
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 7346
 
10.6%
N 6441
 
9.3%
E 6283
 
9.0%
T 5488
 
7.9%
O 5444
 
7.8%
A 4994
 
7.2%
I 4070
 
5.8%
D 3691
 
5.3%
L 3001
 
4.3%
W 2994
 
4.3%
Other values (18) 19831
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 7346
 
10.6%
N 6441
 
9.3%
E 6283
 
9.0%
T 5488
 
7.9%
O 5444
 
7.8%
A 4994
 
7.2%
I 4070
 
5.8%
D 3691
 
5.3%
L 3001
 
4.3%
W 2994
 
4.3%
Other values (18) 19831
28.5%
Distinct268
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:21.270821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length16
Mean length8.8206536
Min length2

Characters and Unicode

Total characters69101
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)0.6%

Sample

1st rowNORWICH
2nd rowHEBRON
3rd rowSHELTON
4th rowENFIELD
5th rowMILFORD
ValueCountFrequency (%)
new 984
 
10.2%
hartford 852
 
8.8%
haven 745
 
7.7%
waterbury 525
 
5.4%
bridgeport 425
 
4.4%
east 289
 
3.0%
britain 272
 
2.8%
west 229
 
2.4%
bristol 195
 
2.0%
meriden 188
 
2.0%
Other values (235) 4932
51.2%
2024-04-18T17:56:22.040826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 7469
 
10.8%
N 6638
 
9.6%
E 6099
 
8.8%
T 5477
 
7.9%
O 5413
 
7.8%
A 4917
 
7.1%
I 3921
 
5.7%
D 3695
 
5.3%
W 3089
 
4.5%
L 2784
 
4.0%
Other values (20) 19599
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 7469
 
10.8%
N 6638
 
9.6%
E 6099
 
8.8%
T 5477
 
7.9%
O 5413
 
7.8%
A 4917
 
7.1%
I 3921
 
5.7%
D 3695
 
5.3%
W 3089
 
4.5%
L 2784
 
4.0%
Other values (20) 19599
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 7469
 
10.8%
N 6638
 
9.6%
E 6099
 
8.8%
T 5477
 
7.9%
O 5413
 
7.8%
A 4917
 
7.1%
I 3921
 
5.7%
D 3695
 
5.3%
W 3089
 
4.5%
L 2784
 
4.0%
Other values (20) 19599
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 7469
 
10.8%
N 6638
 
9.6%
E 6099
 
8.8%
T 5477
 
7.9%
O 5413
 
7.8%
A 4917
 
7.1%
I 3921
 
5.7%
D 3695
 
5.3%
W 3089
 
4.5%
L 2784
 
4.0%
Other values (20) 19599
28.4%
Distinct88
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:22.427966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length9
Mean length9.8068675
Min length3

Characters and Unicode

Total characters76827
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.3%

Sample

1st rowResidence
2nd rowResidence
3rd rowResidence
4th rowIn Vehicle
5th rowResidence
ValueCountFrequency (%)
residence 5571
56.6%
other 561
 
5.7%
or 466
 
4.7%
hotel 420
 
4.3%
motel 420
 
4.3%
residential 270
 
2.7%
building 240
 
2.4%
home 190
 
1.9%
house 189
 
1.9%
unknown 170
 
1.7%
Other values (99) 1349
 
13.7%
2024-04-18T17:56:23.062286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 19989
26.0%
i 7278
 
9.5%
n 7155
 
9.3%
d 6311
 
8.2%
s 6211
 
8.1%
c 5903
 
7.7%
R 5889
 
7.7%
t 2423
 
3.2%
o 2397
 
3.1%
2012
 
2.6%
Other values (40) 11259
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19989
26.0%
i 7278
 
9.5%
n 7155
 
9.3%
d 6311
 
8.2%
s 6211
 
8.1%
c 5903
 
7.7%
R 5889
 
7.7%
t 2423
 
3.2%
o 2397
 
3.1%
2012
 
2.6%
Other values (40) 11259
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19989
26.0%
i 7278
 
9.5%
n 7155
 
9.3%
d 6311
 
8.2%
s 6211
 
8.1%
c 5903
 
7.7%
R 5889
 
7.7%
t 2423
 
3.2%
o 2397
 
3.1%
2012
 
2.6%
Other values (40) 11259
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19989
26.0%
i 7278
 
9.5%
n 7155
 
9.3%
d 6311
 
8.2%
s 6211
 
8.1%
c 5903
 
7.7%
R 5889
 
7.7%
t 2423
 
3.2%
o 2397
 
3.1%
2012
 
2.6%
Other values (40) 11259
14.7%
Distinct396
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:23.464708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length81
Median length15
Mean length14.270232
Min length7

Characters and Unicode

Total characters111793
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)3.5%

Sample

1st rowDrug Use
2nd rowDrug Use
3rd rowDrug Use
4th rowDrug abuse
5th rowIngestion
ValueCountFrequency (%)
substance 4929
30.2%
abuse 4860
29.8%
use 1755
 
10.8%
drug 1593
 
9.8%
ingestion 374
 
2.3%
and 271
 
1.7%
used 247
 
1.5%
medications 215
 
1.3%
injection 165
 
1.0%
took 119
 
0.7%
Other values (182) 1771
 
10.9%
2024-04-18T17:56:24.358106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13627
12.2%
s 13186
11.8%
u 12111
10.8%
b 9732
8.7%
8469
 
7.6%
a 8021
 
7.2%
n 7653
 
6.8%
t 6570
 
5.9%
c 6111
 
5.5%
S 4752
 
4.3%
Other values (46) 21561
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13627
12.2%
s 13186
11.8%
u 12111
10.8%
b 9732
8.7%
8469
 
7.6%
a 8021
 
7.2%
n 7653
 
6.8%
t 6570
 
5.9%
c 6111
 
5.5%
S 4752
 
4.3%
Other values (46) 21561
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13627
12.2%
s 13186
11.8%
u 12111
10.8%
b 9732
8.7%
8469
 
7.6%
a 8021
 
7.2%
n 7653
 
6.8%
t 6570
 
5.9%
c 6111
 
5.5%
S 4752
 
4.3%
Other values (46) 21561
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13627
12.2%
s 13186
11.8%
u 12111
10.8%
b 9732
8.7%
8469
 
7.6%
a 8021
 
7.2%
n 7653
 
6.8%
t 6570
 
5.9%
c 6111
 
5.5%
S 4752
 
4.3%
Other values (46) 21561
19.3%
Distinct231
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:25.058293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length15
Mean length8.8108246
Min length4

Characters and Unicode

Total characters69024
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.5%

Sample

1st rowNORWICH
2nd rowMARLBOROUGH
3rd rowBRIDGEPORT
4th rowENFIELD
5th rowMILFORD
ValueCountFrequency (%)
new 1216
 
12.6%
hartford 1039
 
10.7%
haven 838
 
8.7%
waterbury 615
 
6.4%
bridgeport 523
 
5.4%
britain 304
 
3.1%
east 231
 
2.4%
norwich 221
 
2.3%
bristol 212
 
2.2%
london 207
 
2.1%
Other values (208) 4274
44.2%
2024-04-18T17:56:26.190656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 8068
11.7%
N 6449
 
9.3%
E 6145
 
8.9%
T 5369
 
7.8%
O 5296
 
7.7%
A 4980
 
7.2%
D 3918
 
5.7%
I 3855
 
5.6%
W 3160
 
4.6%
H 2900
 
4.2%
Other values (22) 18884
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 8068
11.7%
N 6449
 
9.3%
E 6145
 
8.9%
T 5369
 
7.8%
O 5296
 
7.7%
A 4980
 
7.2%
D 3918
 
5.7%
I 3855
 
5.6%
W 3160
 
4.6%
H 2900
 
4.2%
Other values (22) 18884
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 8068
11.7%
N 6449
 
9.3%
E 6145
 
8.9%
T 5369
 
7.8%
O 5296
 
7.7%
A 4980
 
7.2%
D 3918
 
5.7%
I 3855
 
5.6%
W 3160
 
4.6%
H 2900
 
4.2%
Other values (22) 18884
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 8068
11.7%
N 6449
 
9.3%
E 6145
 
8.9%
T 5369
 
7.8%
O 5296
 
7.7%
A 4980
 
7.2%
D 3918
 
5.7%
I 3855
 
5.6%
W 3160
 
4.6%
H 2900
 
4.2%
Other values (22) 18884
27.4%
Distinct362
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:26.773341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length9
Mean length9.2717641
Min length1

Characters and Unicode

Total characters72635
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)3.5%

Sample

1st rowHospital
2nd rowHospital
3rd rowHospital
4th rowRoadway in vehicle
5th rowResidence
ValueCountFrequency (%)
residence 4391
46.7%
hospital 2276
24.2%
0 551
 
5.9%
356
 
3.8%
er/outpatient 196
 
2.1%
inpatient 122
 
1.3%
friend's 115
 
1.2%
home 103
 
1.1%
house 98
 
1.0%
inn 62
 
0.7%
Other values (353) 1126
 
12.0%
2024-04-18T17:56:27.748293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14616
20.1%
i 7498
10.3%
s 7175
9.9%
n 5443
 
7.5%
d 4799
 
6.6%
R 4637
 
6.4%
c 4560
 
6.3%
t 3569
 
4.9%
a 3029
 
4.2%
o 2951
 
4.1%
Other values (60) 14358
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14616
20.1%
i 7498
10.3%
s 7175
9.9%
n 5443
 
7.5%
d 4799
 
6.6%
R 4637
 
6.4%
c 4560
 
6.3%
t 3569
 
4.9%
a 3029
 
4.2%
o 2951
 
4.1%
Other values (60) 14358
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14616
20.1%
i 7498
10.3%
s 7175
9.9%
n 5443
 
7.5%
d 4799
 
6.6%
R 4637
 
6.4%
c 4560
 
6.3%
t 3569
 
4.9%
a 3029
 
4.2%
o 2951
 
4.1%
Other values (60) 14358
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14616
20.1%
i 7498
10.3%
s 7175
9.9%
n 5443
 
7.5%
d 4799
 
6.6%
R 4637
 
6.4%
c 4560
 
6.3%
t 3569
 
4.9%
a 3029
 
4.2%
o 2951
 
4.1%
Other values (60) 14358
19.8%
Distinct5199
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-04-18T17:56:28.208510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length258
Median length157
Mean length70.16288
Min length6

Characters and Unicode

Total characters549656
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4656 ?
Unique (%)59.4%

Sample

1st rowHeroin Toxicity
2nd rowHeroin Intoxication
3rd rowAcute Fentanyl Intoxication
4th rowAcute Intoxication Cocaine Toxicity
5th rowAcute Oxycodone Intoxication
ValueCountFrequency (%)
and 6369
 
9.1%
acute 6011
 
8.5%
intoxication 5816
 
8.3%
fentanyl 5678
 
8.1%
of 4653
 
6.6%
combined 4428
 
6.3%
effects 4336
 
6.2%
the 3731
 
5.3%
cocaine 2429
 
3.5%
heroin 2364
 
3.4%
Other values (730) 24524
34.9%
2024-04-18T17:56:29.185745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63178
 
11.5%
n 50540
 
9.2%
e 45244
 
8.2%
o 41902
 
7.6%
t 40574
 
7.4%
i 34573
 
6.3%
a 32144
 
5.8%
c 25145
 
4.6%
d 16993
 
3.1%
l 16327
 
3.0%
Other values (61) 183036
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 549656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
63178
 
11.5%
n 50540
 
9.2%
e 45244
 
8.2%
o 41902
 
7.6%
t 40574
 
7.4%
i 34573
 
6.3%
a 32144
 
5.8%
c 25145
 
4.6%
d 16993
 
3.1%
l 16327
 
3.0%
Other values (61) 183036
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 549656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
63178
 
11.5%
n 50540
 
9.2%
e 45244
 
8.2%
o 41902
 
7.6%
t 40574
 
7.4%
i 34573
 
6.3%
a 32144
 
5.8%
c 25145
 
4.6%
d 16993
 
3.1%
l 16327
 
3.0%
Other values (61) 183036
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 549656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
63178
 
11.5%
n 50540
 
9.2%
e 45244
 
8.2%
o 41902
 
7.6%
t 40574
 
7.4%
i 34573
 
6.3%
a 32144
 
5.8%
c 25145
 
4.6%
d 16993
 
3.1%
l 16327
 
3.0%
Other values (61) 183036
33.3%

Manner of Death
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
Accident
7830 
accident
 
2
Pending
 
1
Natural
 
1

Length

Max length8
Median length8
Mean length7.9997447
Min length7

Characters and Unicode

Total characters62670
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAccident
2nd rowAccident
3rd rowAccident
4th rowAccident
5th rowAccident

Common Values

ValueCountFrequency (%)
Accident 7830
99.9%
accident 2
 
< 0.1%
Pending 1
 
< 0.1%
Natural 1
 
< 0.1%

Length

2024-04-18T17:56:29.464600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:29.737732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
accident 7832
> 99.9%
pending 1
 
< 0.1%
natural 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 15664
25.0%
n 7834
12.5%
i 7833
12.5%
d 7833
12.5%
e 7833
12.5%
t 7833
12.5%
A 7830
12.5%
a 4
 
< 0.1%
P 1
 
< 0.1%
g 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 15664
25.0%
n 7834
12.5%
i 7833
12.5%
d 7833
12.5%
e 7833
12.5%
t 7833
12.5%
A 7830
12.5%
a 4
 
< 0.1%
P 1
 
< 0.1%
g 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 15664
25.0%
n 7834
12.5%
i 7833
12.5%
d 7833
12.5%
e 7833
12.5%
t 7833
12.5%
A 7830
12.5%
a 4
 
< 0.1%
P 1
 
< 0.1%
g 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 15664
25.0%
n 7834
12.5%
i 7833
12.5%
d 7833
12.5%
e 7833
12.5%
t 7833
12.5%
A 7830
12.5%
a 4
 
< 0.1%
P 1
 
< 0.1%
g 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
Distinct255
Distinct (%)34.2%
Missing7089
Missing (%)90.5%
Memory size61.3 KiB
2024-04-18T17:56:30.089035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length96
Median length80
Mean length35.500671
Min length5

Characters and Unicode

Total characters26448
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)27.0%

Sample

1st rowHypertensive and Atherosclerotic Cardiovascular Disease
2nd rowAtherosclerotic and Hypertensive Cardiovascular Disease, Diabetes
3rd rowRecent Cocaine Use
4th rowHypertensive And Atherosclerotic Cardiovascular Disease
5th rowCardiac Hypertrophy
ValueCountFrequency (%)
disease 311
 
10.7%
cardiovascular 273
 
9.4%
hypertensive 247
 
8.5%
cocaine 237
 
8.2%
and 233
 
8.1%
use 223
 
7.7%
atherosclerotic 210
 
7.3%
recent 205
 
7.1%
chronic 80
 
2.8%
acute 53
 
1.8%
Other values (164) 822
28.4%
2024-04-18T17:56:30.808807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3262
12.3%
2149
 
8.1%
a 2041
 
7.7%
i 1955
 
7.4%
s 1914
 
7.2%
r 1742
 
6.6%
c 1623
 
6.1%
o 1541
 
5.8%
t 1490
 
5.6%
n 1379
 
5.2%
Other values (47) 7352
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3262
12.3%
2149
 
8.1%
a 2041
 
7.7%
i 1955
 
7.4%
s 1914
 
7.2%
r 1742
 
6.6%
c 1623
 
6.1%
o 1541
 
5.8%
t 1490
 
5.6%
n 1379
 
5.2%
Other values (47) 7352
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3262
12.3%
2149
 
8.1%
a 2041
 
7.7%
i 1955
 
7.4%
s 1914
 
7.2%
r 1742
 
6.6%
c 1623
 
6.1%
o 1541
 
5.8%
t 1490
 
5.6%
n 1379
 
5.2%
Other values (47) 7352
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3262
12.3%
2149
 
8.1%
a 2041
 
7.7%
i 1955
 
7.4%
s 1914
 
7.2%
r 1742
 
6.6%
c 1623
 
6.1%
o 1541
 
5.8%
t 1490
 
5.6%
n 1379
 
5.2%
Other values (47) 7352
27.8%

Heroin
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
5114 
1
2720 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5114
65.3%
1 2720
34.7%

Length

2024-04-18T17:56:31.082800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:31.299422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5114
65.3%
1 2720
34.7%

Most occurring characters

ValueCountFrequency (%)
0 5114
65.3%
1 2720
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5114
65.3%
1 2720
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5114
65.3%
1 2720
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5114
65.3%
1 2720
34.7%

Heroin death certificate (DC)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7511 
1
 
323

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7511
95.9%
1 323
 
4.1%

Length

2024-04-18T17:56:31.500859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:31.738493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7511
95.9%
1 323
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 7511
95.9%
1 323
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7511
95.9%
1 323
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7511
95.9%
1 323
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7511
95.9%
1 323
 
4.1%

Cocaine
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
5121 
1
2713 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5121
65.4%
1 2713
34.6%

Length

2024-04-18T17:56:31.919050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:32.126806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5121
65.4%
1 2713
34.6%

Most occurring characters

ValueCountFrequency (%)
0 5121
65.4%
1 2713
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5121
65.4%
1 2713
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5121
65.4%
1 2713
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5121
65.4%
1 2713
34.6%

Fentanyl
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
1
5071 
0
2763 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5071
64.7%
0 2763
35.3%

Length

2024-04-18T17:56:32.306220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:32.516860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5071
64.7%
0 2763
35.3%

Most occurring characters

ValueCountFrequency (%)
1 5071
64.7%
0 2763
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5071
64.7%
0 2763
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5071
64.7%
0 2763
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5071
64.7%
0 2763
35.3%

Fentanyl Analogue
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7158 
1
 
676

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7158
91.4%
1 676
 
8.6%

Length

2024-04-18T17:56:32.728398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:32.948049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7158
91.4%
1 676
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 7158
91.4%
1 676
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7158
91.4%
1 676
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7158
91.4%
1 676
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7158
91.4%
1 676
 
8.6%

Oxycodone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7097 
1
737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 7097
90.6%
1 737
 
9.4%

Length

2024-04-18T17:56:33.125524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:33.334512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7097
90.6%
1 737
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 7097
90.6%
1 737
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7097
90.6%
1 737
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7097
90.6%
1 737
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7097
90.6%
1 737
 
9.4%

Oxymorphone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7701 
1
 
133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7701
98.3%
1 133
 
1.7%

Length

2024-04-18T17:56:33.511521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:33.745750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7701
98.3%
1 133
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 7701
98.3%
1 133
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7701
98.3%
1 133
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7701
98.3%
1 133
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7701
98.3%
1 133
 
1.7%

Ethanol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
5748 
1
2086 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5748
73.4%
1 2086
 
26.6%

Length

2024-04-18T17:56:33.922205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:34.135845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5748
73.4%
1 2086
 
26.6%

Most occurring characters

ValueCountFrequency (%)
0 5748
73.4%
1 2086
 
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5748
73.4%
1 2086
 
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5748
73.4%
1 2086
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5748
73.4%
1 2086
 
26.6%

Hydrocodone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7709 
1
 
125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7709
98.4%
1 125
 
1.6%

Length

2024-04-18T17:56:34.318461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:34.533779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7709
98.4%
1 125
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 7709
98.4%
1 125
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7709
98.4%
1 125
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7709
98.4%
1 125
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7709
98.4%
1 125
 
1.6%

Benzodiazepine
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
5951 
1
1883 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5951
76.0%
1 1883
 
24.0%

Length

2024-04-18T17:56:34.711859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:34.947939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5951
76.0%
1 1883
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 5951
76.0%
1 1883
 
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5951
76.0%
1 1883
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5951
76.0%
1 1883
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5951
76.0%
1 1883
 
24.0%

Methadone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7139 
1
 
695

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7139
91.1%
1 695
 
8.9%

Length

2024-04-18T17:56:35.133413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:35.350720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7139
91.1%
1 695
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 7139
91.1%
1 695
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7139
91.1%
1 695
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7139
91.1%
1 695
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7139
91.1%
1 695
 
8.9%

Meth/Amphetamine
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7721 
1
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7721
98.6%
1 113
 
1.4%

Length

2024-04-18T17:56:35.530076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:35.792769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7721
98.6%
1 113
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 7721
98.6%
1 113
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7721
98.6%
1 113
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7721
98.6%
1 113
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7721
98.6%
1 113
 
1.4%

Amphet
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7584 
1
 
250

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7584
96.8%
1 250
 
3.2%

Length

2024-04-18T17:56:35.970961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:36.418808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7584
96.8%
1 250
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 7584
96.8%
1 250
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7584
96.8%
1 250
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7584
96.8%
1 250
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7584
96.8%
1 250
 
3.2%

Tramad
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7607 
1
 
227

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7607
97.1%
1 227
 
2.9%

Length

2024-04-18T17:56:36.605641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:36.833149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7607
97.1%
1 227
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 7607
97.1%
1 227
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7607
97.1%
1 227
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7607
97.1%
1 227
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7607
97.1%
1 227
 
2.9%

Hydromorphone
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7783 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7783
99.3%
1 51
 
0.7%

Length

2024-04-18T17:56:37.012698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:37.228756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7783
99.3%
1 51
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 7783
99.3%
1 51
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7783
99.3%
1 51
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7783
99.3%
1 51
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7783
99.3%
1 51
 
0.7%

Morphine (Not Heroin)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7800 
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7800
99.6%
1 34
 
0.4%

Length

2024-04-18T17:56:37.408391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:37.672399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7800
99.6%
1 34
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 7800
99.6%
1 34
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7800
99.6%
1 34
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7800
99.6%
1 34
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7800
99.6%
1 34
 
0.4%

Xylazine
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7423 
1
 
411

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7423
94.8%
1 411
 
5.2%

Length

2024-04-18T17:56:37.911573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:38.218068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7423
94.8%
1 411
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 7423
94.8%
1 411
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7423
94.8%
1 411
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7423
94.8%
1 411
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7423
94.8%
1 411
 
5.2%

Gabapentin
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7666 
1
 
168

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7666
97.9%
1 168
 
2.1%

Length

2024-04-18T17:56:38.614268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:38.927845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7666
97.9%
1 168
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 7666
97.9%
1 168
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7666
97.9%
1 168
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7666
97.9%
1 168
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7666
97.9%
1 168
 
2.1%

Opiate NOS
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7751 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7751
98.9%
1 83
 
1.1%

Length

2024-04-18T17:56:39.276733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:39.574844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7751
98.9%
1 83
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 7751
98.9%
1 83
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7751
98.9%
1 83
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7751
98.9%
1 83
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7751
98.9%
1 83
 
1.1%

Heroin/Morph/Codeine
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
6420 
1
1414 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6420
82.0%
1 1414
 
18.0%

Length

2024-04-18T17:56:39.914954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:40.301751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6420
82.0%
1 1414
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 6420
82.0%
1 1414
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6420
82.0%
1 1414
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6420
82.0%
1 1414
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6420
82.0%
1 1414
 
18.0%

Other Opioid
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
0
7749 
1
 
85

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7749
98.9%
1 85
 
1.1%

Length

2024-04-18T17:56:40.664583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:41.036951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7749
98.9%
1 85
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 7749
98.9%
1 85
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7749
98.9%
1 85
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7749
98.9%
1 85
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7749
98.9%
1 85
 
1.1%

Any Opioid
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
1
5212 
0
2622 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7834
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5212
66.5%
0 2622
33.5%

Length

2024-04-18T17:56:41.341053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T17:56:41.738663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5212
66.5%
0 2622
33.5%

Most occurring characters

ValueCountFrequency (%)
1 5212
66.5%
0 2622
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5212
66.5%
0 2622
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5212
66.5%
0 2622
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5212
66.5%
0 2622
33.5%

Other
Text

MISSING 

Distinct114
Distinct (%)22.0%
Missing7315
Missing (%)93.4%
Memory size61.3 KiB
2024-04-18T17:56:42.098274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length24
Mean length6.8554913
Min length2

Characters and Unicode

Total characters3558
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)14.1%

Sample

1st rowBupren
2nd rowketamine
3rd rowHYDROMORPH
4th rowBupren
5th rowBupren
ValueCountFrequency (%)
pcp 114
20.3%
bupren 105
18.7%
xylazine 63
11.2%
morphine 42
 
7.5%
hydromorph 20
 
3.6%
buprenor 20
 
3.6%
opiate 18
 
3.2%
u-47700 17
 
3.0%
morph 11
 
2.0%
mdma 11
 
2.0%
Other values (74) 141
25.1%
2024-04-18T17:56:42.782475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 319
 
9.0%
e 275
 
7.7%
n 251
 
7.1%
p 194
 
5.5%
r 194
 
5.5%
i 149
 
4.2%
B 128
 
3.6%
R 125
 
3.5%
u 120
 
3.4%
a 115
 
3.2%
Other values (44) 1688
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 319
 
9.0%
e 275
 
7.7%
n 251
 
7.1%
p 194
 
5.5%
r 194
 
5.5%
i 149
 
4.2%
B 128
 
3.6%
R 125
 
3.5%
u 120
 
3.4%
a 115
 
3.2%
Other values (44) 1688
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 319
 
9.0%
e 275
 
7.7%
n 251
 
7.1%
p 194
 
5.5%
r 194
 
5.5%
i 149
 
4.2%
B 128
 
3.6%
R 125
 
3.5%
u 120
 
3.4%
a 115
 
3.2%
Other values (44) 1688
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 319
 
9.0%
e 275
 
7.7%
n 251
 
7.1%
p 194
 
5.5%
r 194
 
5.5%
i 149
 
4.2%
B 128
 
3.6%
R 125
 
3.5%
u 120
 
3.4%
a 115
 
3.2%
Other values (44) 1688
47.4%

Date
Date

Distinct2798
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
Minimum2012-01-01 00:00:00
Maximum2021-12-31 00:00:00
2024-04-18T17:56:43.081810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-18T17:56:43.345925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-04-18T17:56:15.097647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-18T17:56:43.626147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeAmphetAny OpioidBenzodiazepineCocaineDate TypeEthanolEthnicityFentanylFentanyl AnalogueGabapentinHeroinHeroin death certificate (DC)Heroin/Morph/CodeineHydrocodoneHydromorphoneManner of DeathMeth/AmphetamineMethadoneMorphine (Not Heroin)Opiate NOSOther OpioidOxycodoneOxymorphoneRaceSexTramadXylazine
Age1.0000.0420.0590.0000.0900.0350.0970.0220.1110.0000.0650.0550.0360.0000.0630.0380.0000.0630.0790.0330.0000.0400.0950.0160.0370.0590.0790.004
Amphet0.0421.0000.0110.0560.0000.0090.0000.1190.0240.0140.0390.0370.0000.0490.0000.0000.0000.0150.0040.0000.0000.0530.0050.0000.0280.0290.0000.045
Any Opioid0.0590.0111.0000.0000.0180.5300.0350.2930.5360.0930.0900.1290.1450.3300.0390.0000.0250.0470.0070.0000.0330.0720.0680.0620.0950.0280.0290.165
Benzodiazepine0.0000.0560.0001.0000.1270.0710.0270.0500.0710.0110.0420.0180.0050.0160.0170.0280.0290.0520.1070.0500.0000.0330.1240.0580.1370.1300.0320.012
Cocaine0.0900.0000.0180.1271.0000.0120.0210.0670.0600.0120.0000.0470.0080.0280.0410.0420.0000.0000.0750.0320.0210.0000.1060.0670.1390.0000.0230.032
Date Type0.0350.0090.5300.0710.0121.0000.0000.1960.0000.0590.0530.0910.0760.1760.0000.0380.0380.0420.0060.0350.0000.0360.0000.0250.0700.0000.0000.087
Ethanol0.0970.0000.0350.0270.0210.0001.0000.0360.0060.0000.0260.0320.0000.0070.0090.0230.0090.0110.0390.0000.0000.0000.0130.0000.0340.0510.0000.006
Ethnicity0.0220.1190.2930.0500.0670.1960.0361.0000.2430.0000.2520.2760.0950.1010.0000.0001.0000.0000.0001.0000.0370.0000.1160.0000.1730.0920.0270.296
Fentanyl0.1110.0240.5360.0710.0600.0000.0060.2431.0000.2150.0310.1070.0520.0560.0940.0260.0100.0280.1200.0740.1100.0270.2000.1060.0850.0970.0000.169
Fentanyl Analogue0.0000.0140.0930.0110.0120.0590.0000.0000.2151.0000.0000.0430.0620.0990.0110.0000.0000.0140.0120.0120.0270.0000.0490.0260.0080.0130.0000.000
Gabapentin0.0650.0390.0900.0420.0000.0530.0260.2520.0310.0001.0000.0710.0260.0170.0000.0000.0000.0090.0170.0000.0000.0000.0000.0000.0970.0560.0330.081
Heroin0.0550.0370.1290.0180.0470.0910.0320.2760.1070.0430.0711.0000.2830.5900.0370.0160.0000.0000.0460.0450.0730.0110.1160.0540.1070.0590.0340.116
Heroin death certificate (DC)0.0360.0000.1450.0050.0080.0760.0000.0950.0520.0620.0260.2831.0000.4410.0000.0060.0000.0190.0000.0000.0140.0150.0330.0220.0590.0310.0100.046
Heroin/Morph/Codeine0.0000.0490.3300.0160.0280.1760.0070.1010.0560.0990.0170.5900.4411.0000.0150.0190.0000.0580.0030.0720.1050.0460.0720.0270.0550.0450.0170.079
Hydrocodone0.0630.0000.0390.0170.0410.0000.0090.0000.0940.0110.0000.0370.0000.0151.0000.0320.0870.0000.0000.0000.0000.0000.0470.0000.0860.0420.0000.008
Hydromorphone0.0380.0000.0000.0280.0420.0380.0230.0000.0260.0000.0000.0160.0060.0190.0321.0000.0000.0000.0000.0780.0000.0090.0170.0000.0000.0250.0000.000
Manner of Death0.0000.0000.0250.0290.0000.0380.0091.0000.0100.0000.0000.0000.0000.0000.0870.0001.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.000
Meth/Amphetamine0.0630.0150.0470.0520.0000.0420.0110.0000.0280.0140.0090.0000.0190.0580.0000.0000.0001.0000.0130.0000.0000.0000.0100.0000.0120.0170.0010.023
Methadone0.0790.0040.0070.1070.0750.0060.0390.0000.1200.0120.0170.0460.0000.0030.0000.0000.0000.0131.0000.0000.0180.0000.0270.0000.0450.0780.0000.000
Morphine (Not Heroin)0.0330.0000.0000.0500.0320.0350.0001.0000.0740.0120.0000.0450.0000.0720.0000.0780.0000.0000.0001.0000.0180.0000.0610.0000.0000.0180.0000.000
Opiate NOS0.0000.0000.0330.0000.0210.0000.0000.0370.1100.0270.0000.0730.0140.1050.0000.0000.0000.0000.0180.0181.0000.0000.0000.0000.1040.0040.0000.018
Other Opioid0.0400.0530.0720.0330.0000.0360.0000.0000.0270.0000.0000.0110.0150.0460.0000.0090.0000.0000.0000.0000.0001.0000.0000.0000.0000.0280.0580.037
Oxycodone0.0950.0050.0680.1240.1060.0000.0130.1160.2000.0490.0000.1160.0330.0720.0470.0170.0300.0100.0270.0610.0000.0001.0000.3350.0360.0500.0000.038
Oxymorphone0.0160.0000.0620.0580.0670.0250.0000.0000.1060.0260.0000.0540.0220.0270.0000.0000.0000.0000.0000.0000.0000.0000.3351.0000.0170.0180.0000.021
Race0.0370.0280.0950.1370.1390.0700.0340.1730.0850.0080.0970.1070.0590.0550.0860.0000.0000.0120.0450.0000.1040.0000.0360.0171.0000.0130.0000.083
Sex0.0590.0290.0280.1300.0000.0000.0510.0920.0970.0130.0560.0590.0310.0450.0420.0250.0000.0170.0780.0180.0040.0280.0500.0180.0131.0000.0220.000
Tramad0.0790.0000.0290.0320.0230.0000.0000.0270.0000.0000.0330.0340.0100.0170.0000.0000.0000.0010.0000.0000.0000.0580.0000.0000.0000.0221.0000.005
Xylazine0.0040.0450.1650.0120.0320.0870.0060.2960.1690.0000.0810.1160.0460.0790.0080.0000.0000.0230.0000.0000.0180.0370.0380.0210.0830.0000.0051.000

Missing values

2024-04-18T17:56:15.554386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T17:56:16.521367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-18T17:56:17.028714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Date TypeAgeSexRaceEthnicityResidence CityInjury CityInjury PlaceDescription of InjuryDeath CityLocationCause of DeathManner of DeathOther Significant ConditionsHeroinHeroin death certificate (DC)CocaineFentanylFentanyl AnalogueOxycodoneOxymorphoneEthanolHydrocodoneBenzodiazepineMethadoneMeth/AmphetamineAmphetTramadHydromorphoneMorphine (Not Heroin)XylazineGabapentinOpiate NOSHeroin/Morph/CodeineOther OpioidAny OpioidOtherDate
0Date of death37MaleWhiteNaNNORWICHNORWICHResidenceDrug UseNORWICHHospitalHeroin ToxicityAccidentNaN1000000000000000000000NaN27-06-2012
1Date of death28MaleWhiteNaNHEBRONHEBRONResidenceDrug UseMARLBOROUGHHospitalHeroin IntoxicationAccidentNaN1000000000000000000000NaN24-03-2014
2Date of death41MaleWhiteNaNSHELTONSHELTONResidenceDrug UseBRIDGEPORTHospitalAcute Fentanyl IntoxicationAccidentNaN0001000000000000000001NaN16-01-2016
3Date reported57MaleWhiteNaNBLANDFORDENFIELDIn VehicleDrug abuseENFIELDRoadway in vehicleAcute Intoxication Cocaine ToxicityAccidentNaN0010000000000000000000NaN13-06-2017
4Date reported64MaleWhiteNaNMILFORDMILFORDResidenceIngestionMILFORDResidenceAcute Oxycodone IntoxicationAccidentNaN0000010000000000000000NaN02-02-2017
5Date of death23MaleWhiteNaNBETHELBETHELResidenceInhalationBETHELResidenceHeroin IntoxicationAccidentNaN1000000000000000000000NaN05-08-2013
6Date reported54MaleWhiteNaNMERIDENUNKNOWNUnknownSubstance abuseMIDDLETOWNHospitalAcute Fentanyl IntoxicationAccidentNaN0001000000000000000000NaN14-01-2017
7Date of death45FemaleWhiteNaNMANSFIELDMANSFIELDResidenceUsed HeroinMANSFIELDResidenceHeroin ToxicityAccidentNaN1000000000000000000000NaN17-08-2012
8Date of death41MaleWhiteNaNENFIELDENFIELDResidenceSubstance AbuseENFIELDResidenceAcute Heroin ToxicityAccidentNaN1000000000000000000000NaN21-06-2014
9Date of death48MaleWhiteHispanicMERIDENMERIDENResidenceCocaine useMERIDENHospitalCocaine ToxicityAccidentNaN0010000000000000000000NaN21-04-2012
Date TypeAgeSexRaceEthnicityResidence CityInjury CityInjury PlaceDescription of InjuryDeath CityLocationCause of DeathManner of DeathOther Significant ConditionsHeroinHeroin death certificate (DC)CocaineFentanylFentanyl AnalogueOxycodoneOxymorphoneEthanolHydrocodoneBenzodiazepineMethadoneMeth/AmphetamineAmphetTramadHydromorphoneMorphine (Not Heroin)XylazineGabapentinOpiate NOSHeroin/Morph/CodeineOther OpioidAny OpioidOtherDate
7824Date of death45MaleWhiteNaNNEW BRITAINNEW BRITAINResidenceTook drugs and alcoholNEW BRITAINResidenceAcute intoxication due to the combined effects of oxycodone and alcoholAccidentArteriosclerotic cardiovascular disease, Obesity0000010100000000000001NaN21-05-2021
7825Date of death46FemaleWhiteNaNNAUGATUCKNAUGATUCKHomeSubstance AbuseWATERBURYHospital - ER/OutpatientAcute Para-Fluorofentanyl IntoxicationAccidentRecent Cocaine Use0010100000000000000001NaN16-11-2021
7826Date of death41MaleWhiteOther Spanish/Hispanic/LatinoWATERBURYWATERBURYHomeSubstance abuseWATERBURYHospital - Dead On ArrivalHyperactive delirium Acute Phencyclidine and Ethanol IntoxicationAccidentCardiac Hypertrophy, Obesity0000000100000000000000PCP21-12-2021
7827Date of death50FemaleWhiteNaNWINDSOR LOCKSWINDSOR LOCKSOther Specified PlaceSubstance AbuseWINDSOR LOCKSOther (Specify)Acute Intoxication Due to Fentanyl, Xylazine, Fluoxetine, Amitryiptyline, Tramadol, and GabapentinAccidentNaN0001000000000100110001NaN31-12-2021
7828Date of death54MaleWhiteNot Spanish/Hispanic/LatinoCHESHIRECHESHIREHomeSubstance AbuseCHESHIREDecedent's HomeAcute Intoxication due to Fentanyl and EthanolAccidentNaN0001000100000000000001NaN18-12-2021
7829Date of death56MaleBlack or African AmericanNaNSTAMFORDSTAMFORDOther Indoor AreaSubstance abuseSTAMFORDHallway at friend's resideneAcute Intoxication by the Combined Effects of Cocaine, Phencyclidine, and OxycodoneAccidentNaN0010010000000000000001PCP19-10-2021
7830Date of death48MaleBlack or African AmericanNaNWEST HAVENWEST HAVENResidenceSubstance UseNEW HAVENHospital - InpatientComplications of Acute Substance Intoxication Including Cocaine and AlcoholAccidentNaN0010000100000000000000NaN05-04-2021
7831Date of death59MaleWhiteNaNWETHERSFIELDWETHERSFIELDResidenceSubstance AbuseHARTFORDHospital - ER/OutpatientAcute Intoxication by the Combined Effects of Cocaine, Gabapentin, and CitalopramAccidentAtherosclerotic and Hypertensive Cardiovascular Disease0010000000000000010000NaN15-05-2021
7832Date of death68MaleBlack or African AmericanNaNHARTFORDHARTFORDResidenceSubstance AbuseHARTFORDResidenceAcute Cocaine IntoxicationAccidentNaN0010000000000000000000NaN28-05-2021
7833Date of death29MaleWhiteNot Spanish/Hispanic/LatinoSEYMOURSEYMOURHomeSubstance abuseSEYMOURDecedent's HomeAcute Intoxication by the Combined Effects of Fentanyl, Xylazine, and MethadoneAccidentNaN0001000000100000100001NaN24-12-2021